OTFS-based Robust MMSE Precoding Design in Over-the-air Computation

TL;DR

This paper proposes an OTFS-based robust MMSE precoding scheme for over-the-air computation that effectively mitigates channel estimation errors in high-mobility scenarios, enhancing network efficiency and reliability.

Contribution

It introduces a novel transmission framework with integrated pilot-data signals and develops a robust precoding method that accounts for estimation errors, improving over existing non-robust schemes.

Findings

Significant performance improvement over non-robust precoding in high SNR scenarios.

Effective reduction of CSI feedback overhead through echo-based channel estimation.

Enhanced robustness against channel estimation errors in high-mobility environments.

Abstract

Over-the-air computation (AirComp), as a data aggregation method that can improve network efficiency by exploiting the superposition characteristics of wireless channels, has received much attention recently. Meanwhile, the orthogonal time frequency space (OTFS) modulation can provide a strong Doppler resilience and facilitate reliable transmission for high-mobility communications. Hence, in this work, we investigate an OTFS-based AirComp system in the presence of time-frequency dual-selective channels. In particular, we commence from the development of a novel transmission framework for the considered system, where the pilot signal is sent together with data, and the channel estimation is implemented according to the echo from the access point to the sensor, thereby reducing the overhead of channel state information (CSI) feedback. Hereafter, based on the CSI estimated from the previous frame, a robust precoding matrix aiming at minimizing mean square error in the current frame is designed, which takes into account the estimation error from the receiver noise and the outdated CSI. The simulation results demonstrate the effectiveness of the proposed robust precoding scheme by comparing it with the non-robust precoding. The performance gain is more obvious in a high signal-to-noise ratio in case of large channel estimation errors.